K.N.Toosi University of Technology

Nowadays, the incorporation of sustainability has received growing attention in a supply chain network design for two reasons. First, government policies and environmental regulations have forced producers to design eco-friendly business frameworks. Second, companies have become more interested in recovery activities since they can demonstrate a green image of their operations to the community. In this study, a novel optimization model is developed for a sustainable dual-channel supply chain based on consumer buying behavior. In the proposed model, the price of new products is determined in the competition of direct and indirect sales channels. Then, the price of refurbished products is estimated based on the customer expectations. A bi-objective mixed-integer nonlinear programming model is introduced to minimize the total cost and maximize the social aspects of the proposed supply chain network. The proposed model provides significant managerial insights. It suggests managers to offer suitable dynamic prices for their sales channels based on the different segmentations of their customers, which will ultimately lead to more profitability. In this model, unlike most literature models, the percentage of returned goods is calculated more accurately based on customer behaviors, and managers can predict the potential amount of returned goods more precisely for their strategic plans. Three meta-heuristic algorithms are developed since the proposed model has an NP-hard nature. The validation of proposed model and the efficiency of the developed algorithms are confirmed through the computational results.

To meet the increasing demand for high-performance Li-ion batteries, numerous kinds of anode materials have been recommended to substitute the current industrial carbon materials. Among them, Li4Ti5O12 stands out for its high safety, low-strain property, long cycle life, and eco-friendliness. However, Li4Ti5O12 suffers from low electronic/ionic conductivities restricting its high-rate performance and consequently hindering its commercial application, especially in electric vehicles with a fast charging demand. For alleviating these obstacles, several tailoring strategies could be used including ion doping, morphology control, composite formation, or their combinations. In this review, we have summarized the latest progress using the above approaches to improve the electrochemical performance of Li4Ti5O12 anodes, with a focus on rate capability. The literatures reporting the superior rate performance for Li4Ti5O12-based batteries were summarized from various aspects including the optimum preparation conditions, morphological/structural findings, and the main electrochemical features. Finally, the research gaps and the future perspective are proposed.

Asteroids may contain valuable minerals. A method to exploit asteroid mines is to transfer them closer to the Earth for further mining processes. In this work, we optimally mount a set of fixed-angle spacecraft thrusters on the surface of an asteroid to conduct concurrent detumbling and redirecting to the desired orbit. The optimization objective reconciles the minimum duration of the mission with the minimum required fuel as well as the maximum uniformity of the fuel distribution required for all thrusters. Each thruster can respond to redirection and detumbling commands simultaneously. Redirection and detumbling are performed via the directional adaptive guidance method and PID controllers, respectively, and the weight factors for each orbital element and the gains of the rotational control channels are also optimized in the process. We use the particle swarm optimization algorithm to evaluate the objective function by simulating the entire mission to find the optimal design. The rotational control damps the tumbling of the asteroid without interfering with the simultaneous redirection process and eventually fixes the asteroid in the optimally selected orientation in the inertial reference frame. The rotational velocity and attitude of the asteroid are controlled via separate PID controllers, which are set robustly. We can effectively optimize the mission by collectively tuning both the system’s rotational and redirection behaviors as well as the thrusters’ configuration and optimally selecting the final attitude of the asteroid.

We introduce a high-performance terahertz detector based on the photo-thermoelectric effect (PTE) in graphene. Our study outlines a novel approach to enhance terahertz detection through a photodetector that employs a hybrid structure. This structure combines the localized surface plasmon resonance of dual grating gates with the resonant modes of a Fabry-Perot cavity configuration, facilitating a strong interaction between terahertz light and the active graphene layer, thereby improving light absorption. Our numerical investigation reveals frequency selectivity within the terahertz absorptance spectrum for incident waves with transverse magnetic polarization, leading to near-perfect absorptance of graphene. This substantial absorption creates an amplified thermal gradient across the graphene channel due to localized heat generation from terahertz wave absorption. The detector’s absorption characteristics can be adjusted by altering geometrical parameters and tuning two gate voltages. Furthermore, incorporating dual grating gates to create a pn-junction leads to a non-uniform Seebeck coefficient along the channel, enhancing the generated voltage. At a resonant frequency of 1.6 THz, the detector demonstrates a responsivity of 1.26 V/W and a noise-equivalent power (NEP) of 5.2 nW/√Hz at room temperature, under biasing the two grating gates with the low voltages of ±0.2 V.

This article premises the notion of portraying a smart city energy infrastructure (SCEI) from a people-centric bottom-up view. This is concluded by a system of systems approach in which the coordinated collections of autonomous microenergy hubs construct macroenergy hubs as SCEI in a decentralized manner. Moreover, a reliability-oriented framework is introduced, complying with the performance requirement of customer satisfaction from the people-centric view. Since electric energy as a unique commodity is traded within a continuously operated system, reliability becomes an objective from the start of the planning process (i.e., reliability-oriented planning) while still achieving the performance (technical and quality) requirements demanded nowadays. Hence, the proposed reliability-oriented framework tends to improve customer interruption cost and cost of energy not supplied in the objective function, as well as the Electrical energy index of reliability (EEIR) and system average interruption frequency index as indices for both heat and electrical loads. In the proposed framework, all constituent systems collaborate to create a more functional and reliable SCEI, while each independent system intends to increase its benefit. Furthermore, the proposed framework is formulated as a bilevel mixed-integer linear programming planning problem. This bilevel formulation suffers from nonconvexity in its lower level problem. Therefore, it is transformed into a single-level problem through binary constraint relaxation and primal–dual reformulation. The effectiveness of the proposed model is demonstrated by implementing it to the Dättwil district (Switzerland) as a real urban case study system. Simulation results confirm the effectiveness of the proposed approach due to considerable improvement in reliability performance (5.2% in EEIR, 95% in electrical system average interruption frequency index, 0.1% in heat energy index of reliability (HEIR), and 56% in heat system average interruption frequency index), SCEI-to-grid performance (81% in export to import ratio), and sustainability performance (77.8% in emission and 4.8% in energy loss).

Silty sandy soils usually have low shear strength due to their non-cohesive structure, weak internal bonds, and high porosity. Environmental challenges, such as freeze–thaw (F–T) cycles, also reduce the mechanical characteristics and instability of infrastructures and structures built on these soils. Biopolymers and fibers offer a sustainable solution to improve soil strength and F–T strength. However, while much research focuses on stabilizing silty sand, fewer studies examine the combined effects of biopolymers and fibers on soil properties under F–T cycles. Additionally, the correlation between ultrasonic pulse velocity (UPV) and unconfined compressive strength (UCS) in biopolymer-stabilized and fiber-reinforced soils still needs to be explored. This study examines the stabilization of silty sand using Persian gum (PG) (0.5–3%) and kenaf fibers (KF) (0–1.5%) with lengths of 6, 12, and 18 mm at the curing times of 7, 28, and 90 days. The samples were subjected to F–T cycles (0, 1, 2, 3, 6, and 12). The results showed that the highest UCS was achieved with 2.5% PG and 1% KF (12 mm) after 28 days. After 12 F–T cycles, the UCS reductions were 41% for sample with 2.5% PG and 34% for sample 2.5% PG and 1%KF. The swelling after freezing for the 2.5% PG and 1% KF sample and the 2.5% PG sample was 4.8% and 3.45%, respectively. A correlation between UPV and UCS after various F–T cycles was suggested. The scanning electron microscopy (SEM) analysis revealed increased voids, weakened polymer bonds, and cracks after 12 F–T cycles. Graphical abstract

Global warming poses a significant challenge to humanity, primarily fueled by greenhouse gas emissions like carbon dioxide from combustion. With a growing energy demand, producing energy with minimal pollutants is imperative. Natural gas, with its low carbon-to-hydrogen ratio, emerges as a viable energy source. This study investigates MILD combustion of natural gas while considering radiative heat transfer. Combustion modeling utilizes the EDC model. A new chemical mechanism is used in this study that combines two mechanisms: the GRI 2.11 mechanism and the one employed by Stagni et al. specifically tailored for ammonia/hydrogen combustion. Radiation modeling employs the DO model. The radiative properties of gases are analyzed using the WSGGM. Additives such as hydrogen and ammonia have been added to the fuel and water vapor at different levels to air to identify the optimal combination of methane, hydrogen, and ammonia, as well as the ideal air–water vapor mixture. The primary objective is to achieve the lowest possible carbon dioxide emissions with a combination in this range of percentages. Hydrogen significantly reduces CO2 emissions, followed by ammonia, with water vapor playing a minor role. By validating the Taguchi method in the present study, the results demonstrate the effectiveness of the Taguchi method in emission reduction strategies, highlighting its promise for sustainable combustion engineering practices.

The object segmentation mask’s observation sequence shows the trend of changes in the object’s observable geometric form, and predicting them may assist in solving various difficulties in multi-object tracking and segmentation (MOTS). With this aim, we propose the entangled appearance and motion structures network (EAMSN), which can predict the object segmentation mask at the pixel level by integrating VAE and LSTM. Regardless of the surroundings, each EAMSN keeps complete knowledge about the sequence of probable changes in the seen map of the object and its related dynamics. It suggests that EAMSN understands the item meaningfully and is not reliant on instructive examples. As a result, we propose a novel MOTS algorithm. By employing different EAMSNs for each kind of item and training them offline, ambiguities in the segmentation mask discovered for that object may be recovered, and precise estimation of the real boundaries of the object at each step. We analyze our tracker using the KITTI MOTS and MOTS challenges datasets, which comprise car and pedestrian objects, to illustrate the usefulness of the suggested technique. As a result, we developed distinct EAMSNs for cars and pedestrians, trained using the MODELNET40 and Human3.6 M datasets, respectively. The discrepancy between training and testing data demonstrates that EAMSN is not dependent on training data. Finally, we compared our strategy to a variety of other ways. Compared to the published findings, our technique gets the best overall performance.

In many spine surgeries, pedicle screws are commonly used to stabilize vertebrae, however, loosening can be a complication. Different designs have shown improvements in fixation strength, with self-expandable screws featuring shape memory alloy (SMA) structures being of particular interest. This study aimed to assess the fixation strength of self-expandable pedicle screws made with SMA (specifically Nickel-Titanium) sheets. Three types of screws were evaluated: self-expandable screws with a smooth SMA surface, self-expandable screws with a porous SMA surface, and standard design screws. Each screw underwent pullout tests for comparison. Following the tests, the self-expandable screw with a porous surface exhibited the highest pullout force (1141.83 N), compared to 1056.86 N for the smooth self-expandable screw and 1104.25 N for the standard screw. The dissipated plastic strain energy differed among the screws, with values of 0.073 J for the porous self-expandable screw, 0.065 J for the smooth self-expandable screw, and 0.089 J for the standard pedicle screw. Notably, the porous self-expandable screw showed reduced stress on the bone-screw interface. Improving the mechanical design of pedicle screws could significantly enhance screw-bone fixation strength. The utilization of self-expandable pedicle screws with porous surface SMA sheets demonstrates superior performance, potentially mitigating complications like loosening.

A novel solver that combines the finite volume method and the discrete element method to investigate the interaction between progressive waves and a seabed composed of partially consolidated mud is proposed. The finite volume method is employed to solve the 2D Reynolds-averaged Navier– Stokes equations, employing an arbitrary Lagrangian Eulerian description to simulate the propagation of regular waves. The solver incorporates the kinematic boundary conditions at the free surfaces. Through a series of preliminary test cases, both the finite volume method and the discrete element method demonstrate satisfactory performance and are validated using available experimental data. Then, the presented finite volume method–discrete element method solvers are applied to analyze the interaction between water waves and the seabed, considering incompressible pores. The model is extended to simulate the dynamic interaction between waves and mud by incorporating particle–particle interaction. The partially consolidated mud beds are represented as assemblies of spherical particles. The numerical results are compared with experimental data, specifically focusing on the free surface time series, the hydrodynamic pressure, and the particle velocity components measured by wave gauges, pore pressure transducers, and electromagnetic current measurements, respectively. The results demonstrate a good agreement between the numerical and experimental findings. Notably, the estimation of cell porosity is identified as a crucial factor in achieving the accurate results when comparing the numerical and experimental data.

In treating prostate cancer, distinguishing alpha and beta therapies is vital for efficient radiopharmaceutical delivery. Our study introduces a 3D image-based spatiotemporal computational model that utilizes MRI-derived images to evaluate the efficacy of 225Ac-PSMA and 177Lu-PSMA therapies. We examine the impact of tumor size, diffusion, interstitial fluid pressure (IFP), and interstitial fluid velocity (IFV) on the absorbed doses. An MRI-based geometric model of the tumor and its surrounding environment is initially developed. Subsequently, COMSOL Multiphysics software is utilized to solve convection-diffusion-reaction equations and conduct numerical analyses of blood pressure distribution. This computational methodology provides valuable insights into interstitial fluid patterns and the spatiotemporal distribution of extracellular and intracellular concentrations of 225Ac-PSMA and 177Lu-PSMA. In addition, our study investigates the impacts of increasing tumor size on absorbed doses and mechanisms involved in radiopharmaceutical transport and delivery. Larger tumors have diminished absorbed doses, highlighting the need for customized treatments according to tumor size. Diffusion significantly influences the transport and delivery of radiopharmaceuticals. Additionally, alpha therapy was observed to consistently yield higher absorbed doses within the tumor than beta therapy. This study reveals the complex interplay between radiopharmaceutical properties, the tumor microenvironment, and treatment outcomes. It highlights the potential of 225Ac-PSMA in prostate cancer treatment, advocating for personalized treatment strategies tailored to the specific characteristics of each patient and their tumor.

A very new and highly specialized category of radiotracers that is still growing is radiolabeled peptides. Radiolabeled peptides, or radiopeptides, are powerful elements for diagnostic imaging and radionuclide therapy. These laboratory-manufactured peptides have gained attention due to their unique properties. The tiny structure of these peptides compared to proteins and antibodies makes them favorable regarding their availability through simple synthesis from amino acids, easy uptake by receptors on cancer cells, and high specificity and affinity for high-quality and accurate radio imaging. This study highlighted the potential of technetium-99m-labeled peptides in advancing diagnostic capabilities in directed research in Latin America.

Effects of different filter kernels, namely, spectral cutoff ( $\mathcal {S}$ -filter) and Gaussian ( $\mathcal {G}$ -filter), on the geometrical properties of the subfilter stress (SFS) tensor and the filtered strain-rate (FSR) tensor are analysed in a forced homogeneous isotropic turbulence. Utilizing the Euler angle–axis methodology, it is observed that despite similar mean behaviour, the eigenframe alignment between SFS and FSR exhibits a non-trivially different statistical distribution for two different filters. Besides the eigenframe alignment, the eigenstructure of these tensors is also investigated. It is found that in contrast to the eigenstructure of the FSR which does not show sensitive dependence on the filter kernel type, the eigenstructure of the SFS tensor is significantly influenced by the filter type. Subsequently, the impact of different filter kernels on the subfilter energy flux (SFEF) is investigated. It is observed that energy transfer in $\mathcal {G}$ -filtering is preferably distributed over the forward region, whereas for the $\mathcal {S}$ -filter, the SFEF is more evenly distributed over both forward–backward regions, leading to a heavy energy transfer cancellation. Additionally, by decomposing the SFEF into different partial energy fluxes, it is found that the impact of the $\mathcal {S}$ -filtering on the eigenstructure of the SFS leads to the amplification of the backward energy transfer. Conversely, the $\mathcal {G}$ -filtering amplifies the forward energy transfer by producing a more pronounced alignment between the contractive–extensive eigenvectors.

The growing demand for lightweight and high-strength materials in the aerospace and automotive industries, as well as the need for highly conductive materials such as heat sinks, electrodes and integrated circuits, has fueled the exploration of innovative composites. Metal matrix composites (MMCs) reinforced with graphene offer a promising solution, combining the inherent properties of metals with the unique characteristics of graphene. However, the fabrication of MMCs reinforced with graphene poses several challenges such as poor wettability of graphene within the metal matrix, a non-uniform distribution of graphene and graphene clustering. Various fabrication methods have been used to address these challenges; among them, high-pressure torsion (HPT) is a promising solution due to the introduction of a fine- or even nanograined structure with well-distributed graphene within the matrix through severe shear deformation. Grain boundary strengthening, Orowan bypassing due to the presence of non-shearable graphene particles, stress transfer to the reinforcements and the inherent properties of graphene can also enhance the mechanical properties of the graphene-containing MMCs produced by HPT. On the other hand, HPT negatively affects the electrical conductivity of the metal matrices by increasing the dislocation density and the number of grain boundaries. Nevertheless, graphene can also enhance the electrical conductivity of the composite by endowing the metal matrix with its π electrons. A current comprehensive examination of the literature provides valuable insights into the development of graphene-reinforced metal matrix composites fabricated by HPT and gives additional information on their potential applications.

2-D materials are promising candidates for gas sensing applications due to their high surface to volume ratio. However, graphene and MoS2, two prominent members of these materials, show little sensitivity toward gas molecules such as NH3, CO2, and H2O. In this work, the gas sensing properties of graphene and MoS2 lateral heterostructures are investigated theoretically using density functional theory (DFT) in combination with a non-equilibrium Green’s function (NEGF) formalism. The heterostructure consists of a MoS2 part, which is sandwiched between two graphene sides. There are distinct interfaces between MoS2 and graphene, whereby C-Mo and C-S bonds connect the two materials. The results reveal that CO2 and H2O are weakly adsorbed on different parts of the heterostructure, while NH3 molecules are strongly adsorbed on the C-Mo interface with an energy equal to −1.233 eV. Further analyses reveal that only the adsorbed NH3 at the C-Mo surface leads to significant changes in the electronic structure, even in an atmospheric environment, where O2 molecules are pre-adsorbed at the interface. The planar average of electrostatic potential and the calculated currents at ±0.5 V applied voltages reveal that the Schottky barrier at C−Mo graphene/MoS2 interface is very sensitive to the adsorption of NH3 gas molecule.

The sequestration of carbon dioxide (CO2) in deep saline aquifers is recognized as a method for decreasing atmospheric greenhouse gas levels. The injection of large quantities of supercritical CO2 into these deep formations has different degrees of hydraulic, chemical, thermal, and mechanical consequences. The methodology comprises of using the Element Free Galerkin (EFG) modeling technique to study the various effects of CO2 gas injection. The modeled pressure and CO2 saturation are essential for assessing the dynamic of aquifer, bottom pressure trends, and its CO2 storage potential. The coupled approach solves four key equations—system equilibrium, continuity for fluids in porous media, and energy balance—to determine displacement, fluid pressures, and temperature. Here, this research is focused on evaluating the capability of a newly in-house numerical tool for simulation. A coupled thermal-hydro-mechanical model (THM), as an improvement of the isotherm hydromechanical model in their previous work, for geological sequestration of carbon dioxide followed by stress, deformation, and shear-slip failure analyses. The simulation considers the migration of two immiscible fluid phases, CO2 under supercritical conditions and salt water, within a deformable porous medium in fully coupled THM conditions. A 3D simulation from Salah region of Algeria elucidated crucial geomechanical variables such as displacement, pressure, and temperature, with sensitivity analysis identifying key determinants of outlet. The simulation results showcased the proficiency of the in-house software in CO2 injection and sequestration. This emphasized the indispensable role of THM analysis in understanding subsurface impacts and mitigating environmental risks related to reservoir pressures and temperatures.